1. Field of the Invention
The present invention relates to image processing, and more particularly, to a method and apparatus for converting a brightness level of an image into a bi-valued brightness level.
2. Description of the Related Art
Binarization is needed, for example, to convert input image data having one (hereinafter, a multi-valued brightness level) of 256 brightness levels expressed in an 8 bit system into output image data having a black or white brightness level (hereinafter, referred to as a bi-valued brightness level).
Conventional binarization methods include an error diffusion method, as described, for example, in “An Adaptive Algorithm for Spatial Grayscale” by R. Floyd and L. Steinberg, Processings of the SID Vol. 17(2), 75–77 (1976) or in Kang, Harry R., editor, Digital Color Halftoning, Peerless Systems Corporation, SPIE Optical Engineering Press. The error diffusion method modifies a multi-valued brightness level [x(m,n)] to determine a modified brightness level [u(m,n)], and compares the modified brightness level [u(m,n)] against a threshold value [T(m,n)] to determine a bi-valued brightness level [y(m,n)]. Here, m and n correspond to an input pixel and indicate a position of a dot that is expressed on output image data. An input pixel has a multi-valued brightness level [x(m,n)] and is expressed by input image data. A weight value [w(k,l)] is reflected to an error value between u(m,n) and y(m,n) to obtain a modified multi-valued brightness level [u(m,n)]. This error diffusion method causes a worm artifact, which is a particular worm-shaped pattern unpleasant to the eye in a bright multi-valued brightness level, and a start-up artifact. The error diffusion method also causes artifacts having directivity in an intermediate multi-valued brightness level. Indices for estimating the performance of binarization include a factor of homogeneity of dots expressed on output image data, a frequency of generating specific artifacts unpleasant to the eye, and/or a factor of a reduction of slow response that dots appear slowly around where the brightness changes rapidly.
Other conventional brightness level conversion methods for binarization include U.S. Pat. No. 5,535,019 to Eschbach, U.S. Pat. No. 6,160,921 to Marcu and U.S. Pat. No. 5,917,614 to Levien.
U.S. Pat. No. 5,535,019 to Eschbach suggests a conventional brightness level conversion method in which error diffusion threshold imprint values are generated in response to previously-output pixels. Where the multi-valued brightness level [x(k,l)] of a pixel at the position (k,l) is converted into a bi-valued brightness level, the weight sum of the error diffusion threshold imprint values generated using previously-output pixels is used in the calculation of a bi-valued threshold value [T(k,l)]. This conventional brightness level conversion method increases the amount of calculation in the process for generating error diffusion threshold imprint signals, and cannot provide uniform dot distribution because the relative positions between dots are not considered.
U.S. Pat. No. 6,160,921 to Gabriel G. Marcu discloses a conventional brightness level conversion method in which a minimum range where no black dots or white dots must exist around the position (m,n) is set depending on the value of x(m,n). If the multi-valued brightness level [x(m,n)] of an arbitrary pixel is greater than or equal to 128, it is checked whether black dots exist within a predetermined range. If no black dots exist, y(m,n) is binarized to black. If black dots exist, y(m,n) is binarized to white. On the other hand, if [x(m,n)] is less than 128, it is checked whether white dots exist within a predetermined range. If no white dots exist, y(m,n) is binarized to white. If white dots exist, y(m,n) is binarized to black. This conventional brightness level conversion method fixes the relative position between adjacent dots, thus including the problem that output image data converted from input image data having identical brightness levels gain a single pattern. The eyes of a human easily recognize this binarized output image data formed in a single pattern, resulting in an unnatural feeling.
U.S. Pat. No. 5,917,614 to Raphael L Levien discloses another conventional brightness level conversion method that previously determines an optimum distance between dots according to x(m,n). If x(m,n) is greater than or equal to 128, a distance between a black dot for x(m,n) and its most adjacent black dot is calculated. If x(m,n) is less than 128, a distance between a white dot for x(m,n) and its most adjacent white dot is calculated. The difference between the calculated distance and a predetermined distance is weighted to change a bi-valued threshold value. In this conventional brightness level conversion method, as x(m,n) approaches 128, a number of black or white dots increases, such that the optimum distance between dots decreases. Accordingly, the relative position between dots is limited on a digital lattice, resulting in a pattern unpleasant to the eyes. Where a dark multi-valued brightness level having an optimum distance a is converted into a bright multi-valued brightness level having an optimum distance b, a dot for an input pixel with a right multi-valued brightness level is binarized to be isolated by the optimum distance b from neighboring dots. Around where a dark multi-valued brightness level changes to a bright multi-valued brightness level, this conventional brightness level conversion method does not generate dots within the optimum distance b, thus producing a void region, which degrades an image quality of output image data.